Various methods and systems have been developed for reducing background noise in adverse audio environments in which a high level of noises is mixed with a signal. For example, stationary noise suppression techniques are used, in which an output level of noise is proportionally lower relative to the input noise level. Typically, the stationary noise suppression is in the range of 12-13 decibels (dB). The noise suppression is fixed to this conservative level in order to avoid creating undesirable speech distortion, which would be apparent for this technique with higher noise suppression.
In order to provide higher noise suppression, dynamic noise suppression systems based on signal-to-noise ratios (SNR) have been utilized. Unfortunately, SNR, by itself, is not a very good predictor of an amount of speech distortion because of the existence of different noise types in the audio environment and the non-stationary nature of a speech source (e.g., people). SNR is a ratio of how much louder speech is than noise. The SNR may be adversely impacted when speech energy (i.e., the signal) fluctuates over a period of time. The fluctuation of the speech energy can be caused by changes of intensity and sequences of words and pauses.
Additionally, stationary and dynamic noises may be present in the audio environment. The SNR averages all of these stationary and non-stationary noises and speech. There is no consideration as to the statistics of the noise signal; only to the overall level of noise.
In some prior art systems, a fixed classification threshold discrimination system may be used to assist in noise suppression. However, fixed classification systems are not robust. In one example, speech and non-speech elements may be classified based on fixed averages. However, if conditions change, such as when the speaker moves the microphone away from their mouth or noise suddenly gets louder, the fixed classification system will erroneously classify the speech and non-speech elements. As a result, speech elements may be suppressed and overall performance may significantly degrade.